Search Results (764)

To address the limitations of the traditional Balancing Composite Motion Optimization (BCMO) algorithm—namely weak directional global exploration, insufficient local exploitation accuracy, and a tendency to fall into local optima with reduced population diversity in feature selection tasks—this paper proposes a Multi-Strategy Enhanced Balancing Composite Motion Optimization algorithm (MEBCMO). From a symmetry perspective, MEBCMO exploits the symmetric and asymmetric relationships among candidate solutions in the search space to achieve a better balance between exploration and exploitation. The performance of MEBCMO is enhanced through three complementary strategies. First, an adaptive heat-conduction search mechanism is introduced to simulate thermal transmission behavior, where a Sigmoid function adjusts the heat-conduction coefficient α_T from 0.9 to 0.2 during iterations. By utilizing the symmetric fitness–distance relationship between the current solution and the global best, this mechanism improves the directionality and efficiency of global exploration. Second, a quadratic interpolation search strategy is designed. By constructing a quadratic model based on the current individual, a randomly selected individual, and the global best, the algorithm exploits local symmetric characteristics of the fitness landscape to strengthen local exploitation and alleviate performance degradation in high-dimensional spaces. Third, an elite population genetic strategy is incorporated, in which the top three individuals generate new candidates through symmetric linear combinations with non-elite individuals and Gaussian perturbations, preserving population diversity and preventing premature convergence. To evaluate MEBCMO, extensive global optimization experiments are conducted on the CEC2017 benchmark suite with dimensions of 30, 50, and 100, and comparisons are made with eight mainstream algorithms, including PSO, DE, and GWO. Experimental results demonstrate that MEBCMO achieves superior performance across unimodal, multimodal, hybrid, and composite functions. Furthermore, MEBCMO is combined with LightGBM to form the MEBCMO-LightGBM model for feature selection on 14 public datasets, yielding lower fitness values, higher classification accuracy, and fewer selected features. Statistical tests and convergence analyses confirm the effectiveness, stability, and rapid convergence of MEBCMO in symmetric and complex optimization landscapes. Full article

This study establishes a rigorous theoretical framework for Particle Swarm Optimization (PSO) convergence by introducing a novel symmetry assumption governing the algorithm’s stochastic components and a monotonicity condition between function values and Euclidean distance to the global optimum. Under this assumption, we prove linear convergence in expectation and almost sure linear convergence for a modified PSO algorithm with symmetric zero-mean random coefficients when parameters satisfy the explicit condition $w+{\displaystyle \frac{8(c_1^2+c_2^2){\sigma }_r^2}{1-w}}<1$. This provides the first closed-form relationship between inertia weight w, learning factors $c_1,c_2$, and random variance ${\sigma }_r^2$ that guarantees convergence. Building on this theoretical foundation, we develop three hierarchical applications: (1) static parameter design that replaces empirical tuning with theoretical calculation from desired convergence rates; (2) symmetric random factor optimization that eliminates directional bias and stabilizes velocity dynamics while preserving exploration variance; and (3) dynamic adaptive strategies that adjust parameters in real-time based on particle dispersion feedback. By bridging the gap between empirical performance and theoretical guarantees, this work transforms PSO from an empirically driven heuristic into a provably convergent optimization tool with rigorous performance guarantees for objective functions satisfying strict monotonicity between fitness and distance to the optimum (e.g., strictly convex functions). Full article

Life is defined by self-governing networks of molecules that change conformation cyclically, converting thermodynamic motion into directional work and structure. A spectrum of scale, from nanoscopic to macroscopic, involves a shift from intracellular thermodynamically driven processes (thermal agitation ultimately rooted in quantum phenomena) to intercellular bulk flows described by classical physics; from short-distance transport involving diffusion and cytoskeletal transport to long-distance pressure fluxes in hydraulic networks. A review of internal transport systems in macroscopic eukaryotes suggests that a key evolutionary step favoring large size and multicellularity involved exploiting molecular-scale stochasticity to generate organized bulk flows (e.g., motor proteins collectively generating mechanical pressures in metazoan tissues such as cardiac muscle; within tracheophytes, active and passive phloem loading/unloading inducing pressure gradients, and active regulation enabling passive xylem function and hydraulic reliability; sieve-like conduction in heterokonts; and peristaltic shuttle streaming in myxogastrian plasmodia). Macroscopic physiologies are underpinned by Brownian molecular thermodynamics and thus quantum mechanics; the apparently disparate physiologies of large organisms share a fundamental operating principle at small scales. However, the specific translocation mechanisms that extend this functioning to larger scales are embroiled in bauplans, representing phylogenetic constraints to body size. Full article

Artificial light at night (ALAN) is altering nocturnal ecosystems. While the effects of direct light sources on insect behavior are well studied, the influence of large-scale skyglow on migratory orientation remains unclear. Here, we tested how skyglow-induced luminance gradients influence the flight orientation of the fall armyworm, Spodoptera frugiperda, a globally invasive nocturnal migrant that performs seasonal migration in China, using controlled indoor simulations and field assays. Surprisingly, individuals consistently oriented toward darker regions, suggesting that luminance gradients may influence their heading away from the expected seasonal migratory direction. This response was highly consistent across both settings, indicating that skyglow-generated luminance gradients can function as directional cues and potentially interfere with seasonal orientation processes. Such gradients may thus function as ecological traps and represent an underrecognized factor in nocturnal insect navigation. Our findings point to a previously overlooked pathway through which skyglow may affect long-distance orientation in nocturnal migrants, underscoring the need for further work to evaluate its ecological significance within light-polluted environments. Full article

Spatial distribution similarity analysis has extensive application value in multiple domains including geographic information science, urban planning, and engineering site selection. However, traditional regional similarity analysis methods face three key challenges: high sensitivity to directional changes, limitations in feature interpretability, and insufficient adaptability to multi-type data. Addressing these issues, this paper proposes a rotation-invariant spatial distribution similarity analysis method based on ring vectors. This method comprises three stages. First, the traversal starting point of the ring vector is dynamically selected based on the maximum value point of the regional feature matrix. Next, concentric ring features are extracted according to this starting point to achieve multi-scale characterization. Finally, the bidirectional weighted comprehensive distance of ring vectors between regions is calculated to measure the similarity between regions. Three experimental sets verified the method’s effectiveness in terrain matching, engineering site selection, and urban functional area identification. These results confirm its rotational invariance, feature interpretability, and adaptability to multi-type data. This research provides a new technical approach for spatial distribution similarity analysis, with significant theoretical and practical implications for geographic information science, urban planning, and engineering site selection. Full article

As critical national energy arteries, long-distance large-scale cross-regional gas transmission networks are characterized by high operating pressures, extensive spatial coverage, and complex topological structures. Thus, the multi-hazard profiles threatening its safety and reliability operation differ significantly from those of local urban gas distribution networks. This research develops a resilience assessment framework capable of quantifying resistance, adaptation, and recovery capacities of such energy systems. The framework establishes performance indicator systems based on design parameters, installation environments, and construction methods for long-distance trunk pipelines and key facilities such as storage facilities. Furthermore, based on complex network theory, the size of the largest connected component and global efficiency of the transmission network are selected as core topological metrics to characterize functional scale retention and transmission efficiency under disturbances, respectively, with corresponding quantification methods proposed. A cross-regional pipeline transmission network within a representative municipal-level administrative region in China is used as a case for empirical analysis. The quantitative assessment results of pipeline and network resilience are analyzed. The research indicates that trunk pipeline resilience is significantly affected by characteristic parameters, the laying environment, and installation methods. It is notably observed that installation methods like jacking and directional drilling, used for road or river crossings, offer greater resistance than direct burial but considerably lower restoration capacity due to the complexity of both the environment and the repair processes, which increases time and cost. Moreover, simulation-based comparison of recovery strategies demonstrates that, in this case, a repair-time-prioritized strategy more effectively enhances overall adaptive capacity and restoration efficiency than a node-degree-prioritized strategy. The findings provide quantitative analytical tools and decision-support references for resilience assessment and optimization of cross-regional energy transmission networks. Full article

Background/Objectives: Community ambulation after stroke depends on locomotor capacity and confidence in everyday environments. We compared functional performance across three community walking levels and identified constructs independently associated with being an independent community walker in individuals with chronic stroke. Methods: Adults admitted to an acute-care general hospital or an inpatient rehabilitation hospital were enrolled. Community walking level was classified by a self-reported questionnaire. Primary constructs were gait speed, gait endurance, and balance self-efficacy measured with standard clinical tests. Additional measures described balance, lower-limb motor function, and task-based mobility. Group differences were examined with one-way analysis of variance with Bonferroni comparisons. Community walking status was modeled with binary logistic regression using forward stepwise selection. Results: Fifty-nine individuals were analyzed. Performance differed across levels. Effect sizes were small, medium, or large overall. Independent community walkers showed faster gait speed, longer walking distance, and higher balance self-efficacy, with the same direction for balance and lower-limb motor scores and shorter times on task-based tests. In univariable models, age, sex, and time since stroke were not associated with independence, whereas assistive device use related to lower odds. In the multivariable model, gait speed, gait endurance, and balance self-efficacy retained independent associations with independent community walking. Other measures were not retained after adjustment. Conclusions: Community walking status in chronic stroke relates most closely to gait speed, gait endurance, and balance self-efficacy. Evaluation can emphasize the 10 m Walk Test, 6 Min Walk Test, and Activities-specific Balance Confidence Scale, with impairment and task-based tests used to guide intervention planning. Full article

This paper presents a braking force coordination control strategy for electric vehicles based on a hierarchical control architecture. The proposed strategy integrates electronic brakeforce distribution (EBD), direct yaw control (DYC), anti-lock braking system (ABS), and braking force reconstruction functions to effectively enhance braking stability under brake actuator failure conditions. First, a full-vehicle model is established to investigate the braking force coordination process during braking. Then, by analyzing the coupling relationship between the yaw moment and DYC control, a dynamic ABS/DYC coordination strategy is developed. A dynamic computation model of the braking force limited weight coefficient is established, and a three-level braking force coordination mechanism is constructed according to the braking force limited state of each wheel. This mechanism achieves integrated coordination and reconstruction of longitudinal and lateral braking forces. Considering road adhesion, failure sequence, and failure location, eleven typical verification scenarios are designed. Simulation results show that, compared with uncoordinated control methods, the proposed method not only can effectively handle with muti-wheel failure scenarios, but also can reduce the braking distance by up to 7.05% and the lateral deviation by 26.74%, effectively improving the braking safety of electric vehicles. Full article

Path planning is a critical capability for unmanned aerial vehicles (UAVs) operating in complex 2D environments such as agricultural fields or indoor facilities—scenarios where flight altitude is often constrained and safe, smooth trajectories are essential. While the sampling-based Bidirectional RRT* (BI-RRT*) algorithm offers asymptotic optimality and improved computational efficiency, it frequently generates paths that lack the curvature continuity, obstacle clearance, and low turning angles required for stable drone flight. To address these limitations, this paper proposes a bi-directional rapid exploration random tree algorithm based on cooperative expansion strategy (CE-BI-RRT*) specifically designed for UAVs path planning in cluttered 2D settings. In terms of expansion, for different environments, the algorithm successively tests the direct expansion strategy, the intelligent deflection strategy and the improved artificial potential field method, as these strategies can quickly guide the two trees to the target while avoiding obstacles. In terms of ChooseParent and Rewire, the path length, path smoothness and safety distance are comprehensively considered in the path cost function, and a rotation strategy is applied to make the path away from obstacles after rewiring, so as to realize the gradual optimization of the path. The final path is further refined using a cubic Bezier curve optimization technique to ensure smooth transitions and continuous curvature. Evaluation results confirm its search performance when benchmarked against mainstream randomized motion planning algorithms. Full article

Forest fires continue to pose a significant threat to public and personal safety. Detecting smoke in its early stages or when it is distant from the camera is challenging because it appears in only a small region of the captured images. This paper proposes a small-scale smoke detection algorithm called TCSN-YOLO to address these challenges. First, it introduces a novel feature fusion module called trident fusion (TF), which is innovatively designed and incorporated into the neck of the model. TF significantly enhances small target smoke recognition. Additionally, to obtain global contextual information with high computational efficiency, we propose a Cross Attention Mechanism (CAM). CAM captures diverse smoke features by assigning attention weights in both horizontal and vertical directions. Furthermore, we suggest using SoftPool to preserve more detailed information in the feature map. Normalized Wasserstein Distance (NWD) metric be embedded into the loss function of our detector to distinguish positive and negative samples under the same threshold. Finally, we evaluate the proposed model using AI For Humankind dataset and FlgLib dataset. The experimental results demonstrate that our method achieves 37.1% APs, 90.3% AP50, 40.4% AP50:95, 45.34 M Params and 170.5 G FLOPs. Full article

The aerial game environment is complex. To enhance mission success rates, UAVs must comprehensively consider threats from various directions and distances, as well as autonomous evasion maneuver decision-making methods for multiple UAV platforms, rather than solely focusing on threats from specific directions and distances or decision-making methods for fixed UAV platforms. Accordingly, this study proposes an autonomous missile evasion maneuver decision-making method for UAVs, suitable for multi-scenario and multi-platform transferable mission requirements. A three-dimensional UAV-missile pursuit-evasion model is established, along with state-space, hierarchical maneuver action space and reward function models for autonomous missile evasion. The auto-regressive multi-hybrid proximal policy optimization (ARMH-PPO) algorithm is proposed for this model, integrating autoregressive network structures and utilizing long short-term memory (LSTM) networks to extract temporal features. Drawing on exploration curriculum learning principles, temporal fusion of process and event reward functions is implemented to jointly guide the agent’s learning process through human experience and strategy exploration. Additionally, a proportion integration differentiation (PID) method is introduced to control the UAV’s maneuver execution, reducing the coupling between maneuver control quantities and the simulation object. Simulation experiments and result analysis demonstrate that the proposed algorithm ranks first in both average reward value and average evasion success rate metrics, with the average evasion success rate approximately 8% higher than the second-ranked algorithm. In the three initial scenarios where the missile is positioned laterally, head-on, and tail-behind the UAV, the UAV’s missile evasion success rates are 95%, 70%, and 85%, respectively. Multi-platform simulation results demonstrate that the decision model constructed in this paper exhibits a certain degree of multi-platform transferability. Full article

Objectives: Existing motor imagery electroencephalography (MI-EEG) decoding approaches are constrained by their reliance on sole representations of brain connectivity graphs, insufficient utilization of multi-scale information, and lack of adaptability. Methods: To address these constraints, we propose a novel Local–Partition–Global Graph learning Network (LPGGNet). The Local Learning module first constructs functional adjacency matrices using partial directed coherence (PDC), effectively capturing causal dynamic interactions among electrodes. It then employs two layers of temporal convolutions to capture high-level temporal features, followed by Graph Convolutional Networks (GCNs) to capture local topological features. In the Partition Learning module, EEG electrodes are divided into four partitions through a task-driven strategy. For each partition, a novel Gaussian median distance is used to construct adjacency matrices, and Gaussian graph filtering is applied to enhance feature consistency within each partition. After merging the local and partitioned features, the model proceeds to the Global Learning module. In this module, a global adjacency matrix is dynamically computed based on cosine similarity, and residual graph convolutions are then applied to extract highly task-relevant global representations. Finally, two fully connected layers perform the classification. Results: Experiments were conducted on both the BCI Competition IV-2a dataset and a laboratory-recorded dataset, achieving classification accuracies of 82.9% and 87.5%, respectively, which surpass several state-of-the-art models. The contribution of each module was further validated through ablation studies. Conclusions: This study demonstrates the superiority of integrating multi-view brain connectivities with dynamically constructed graph structures for MI-EEG decoding. Moreover, the proposed model offers a novel and efficient solution for EEG signal decoding. Full article

Background: The management of acute ischemic stroke (AIS) in anticoagulated patients presents a clinical challenge, as concerns about safety and efficacy often limit access to recanalization therapies. Despite the widespread use of direct oral anticoagulants (DOACs) and vitamin K antagonists (VKAs), their impact on functional recovery and mortality following intravenous thrombolysis (IVT) and mechanical thrombectomy (MT) remains uncertain. Therefore, this study investigates the association between prior anticoagulation and 90-day outcomes in AIS patients undergoing reperfusion therapy. Methods: We conducted a retrospective cohort analysis using our institutional stroke registry, including AIS patients admitted to the Department of Neurology at our university between February 2023 and 2025. Anticoagulated patients were 1:1 propensity score-matched with non-anticoagulated controls (n = 126 per group) using Mahalanobis distance matching with a caliper, adjusting for age, sex, hypertension, diabetes, stroke severity (National Institutes of Health Stroke Scale [NIHSS] at admission and 72 h), and pre-stroke functional status (pre-morbid modified Rankin Scale [pre-mRS]). Primary endpoints at 90 days were functional independence (modified Rankin Scale [mRS] ≤ 2), mRS-shift, and mortality (mRS = 6). Predictors of outcome were assessed using multivariable logistic regression and generalized additive models (GAMs). Subgroup analyses evaluated the effects of anticoagulation type and treatment modality. Results: Among 866 AIS patients (DOAC n = 100, VKA n = 48, non-anticoagulated n = 718), 426 (49.2%) underwent reperfusion therapy (IVT n = 195, MT n = 163, IVT + MT n = 68). Before matching, anticoagulated patients were less likely to achieve functional independence (34.5% vs. 52.1%, odds ratio [OR] = 0.48, 95% confidence interval [CI] [0.33–0.70], p < 0.001), had a greater mRS-shift (2.53 vs. 1.79, p < 0.001), and higher mortality (30.4% vs. 14.5%, OR = 2.58, 95% CI [1.72–3.88], p < 0.001). However, after matching, these differences were no longer statistically significant. NIHSS, 72hNIHSS, and pre-mRS were the strongest independent predictors of outcome (p < 0.001), while anticoagulation status had no significant effect. Conclusions: Recanalization therapy was not associated with worse functional outcomes in selected anticoagulated AIS patients. These findings suggest that prior anticoagulation alone should not preclude reperfusion therapy in otherwise eligible patients, and underscore the importance of individualized, evidence-based decision-making in acute stroke care. Full article

Despite tremendously valuable work on the T-stub, its safety and reliability in post-fire conditions remain a major concern. It is well known that steel is sensitive to high temperatures. Material degradation at high temperatures is likely to cause the T-stub to yield or gradually collapse, potentially leading to the failure of the entire structure. Recent studies have shown that steel joints exhibit a significant change in moment-rotational response post-fire, as the joint’s load–displacement behavior and failure modes change with increasing exposed temperature. However, studies on T-stubs at high post-fire temperatures are very limited. In this study, the aim is to investigate the post-fire load–displacement curves, ductility, plastic, and ultimate capacities of the unstiffened T-stub connected to a rigid base as a function of the exposed temperature. Of the 36 unstiffened T-stubs tested, 30 were subjected to high temperatures. The selected temperature values were 400 °C, 600 °C, 800 °C, 1000 °C, and 1200 °C. A thin plate of 10 mm was selected for the flange of the T-stub in order to obtain mode 1 behavior. Bolts of M16 and M24 were utilized in order to investigate the effects of bolt diameter on the behavior due to the change in distance of plastic hinges. Furthermore, the distances from a T-stub stem to bolt row (p_f) of 40 mm, 60 mm, and 80 mm were selected. As p_f values decrease, the plastic capacity increases, while the ultimate displacement capacity and the ductility decrease. A direct relation between p_f and yield displacement, and between pf and ultimate capacity, was not detected. As the applied temperature increases, the yield displacement increases and the ductility decreases. No significant change in either the plastic or ultimate capacity was observed up to 400 °C. At higher exposed temperatures, the plastic and ultimate capacity decrease as the applied elevated temperature increases. A significant reduction in the plastic and ultimate capacity was especially observed after post-fire exposure to 1000 °C and 1200 °C. The effects of elevated temperature are more pronounced for the plastic capacity of materials. Reduction factors for both plastic and ultimate capacities were proposed to account for the post-fire effects. The proposed reduction factors can predict the effects of a post-fire environment with high accuracy. The results were compared with AISC 358 and Eurocode 3, and it was revealed that the current standards underestimate the actual capacities. A modified calculation, including a reduction factor, is proposed to obtain more accurate results of unstiffened T-stubs for post-fire conditions. Full article

The purpose of this paper is to implement a path-following control system based on the kinematics of the Lizard-Inspired Single-Actuated robot (LISA). LISA is a new type of robot that mimics the quadrupedal walking morphology of lizards with a four-bar linkage mechanism and can realize both propulsion and turning with 1 degree-of-freedom. To achieve this purpose, this paper takes 3 approaches: kinematics formulation, control system design, and experimental verification. In the kinematics formulation, we formulate LISA’s turning angle, stride length, posture, propulsive direction, curvature, and position coordinate. In control system design, we design a control system that converges not only the distance error but also the posture error and control input. Conditional equations that can achieve these 3 control targets are formulated using forward kinematics and reference path functions. The experimental verifications were carried out to verify the effectiveness of the designed path-following control system using three types of paths: linear, circular, and combined linear and circular. As a result, it was confirmed that the Root Mean Square values for the control input, the distance error, and the attitude error were sufficiently small in steady state. Therefore, it was confirmed that the 3 control objectives had been achieved. Full article